{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T01:08:57Z","timestamp":1783127337776,"version":"3.54.6"},"reference-count":10,"publisher":"National Library of Serbia","issue":"3","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2022]]},"abstract":"<jats:p>This paper expounds on the research status of data mining and the status quo of college students? psychological health problems, deeply analyzing the feasibility of introducing data mining technology into the analysis of college students? psychological health. After studying and analyzing the decision tree technology of data mining, and taking the psychological health problem data of the students in a university in 2021 as the research object, this paper uses the decision tree to analyze the psychological health problem data. The main work includes the following: determining the mining object and mining target; preprocessing the original data; and according to the characteristics of the data used, choosing the C4.5 algorithm of the decision tree to construct the decision tree of the students. Finally, based on the analysis and comparison of the decision tree model before and after pruning, classification rules are extracted from the optimal decision tree model, thus providing a scientific decision-making basis for mental health education in colleges and universities. After comparing the classification results with the known categories in the test set, the accuracy rate was found to be 75%. Using the alternative error pruning method and test data set, the classification accuracy was 79%, and after PEP pruning was 84%.<\/jats:p>","DOI":"10.2298\/csis210404044y","type":"journal-article","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T10:05:27Z","timestamp":1663927527000},"page":"1583-1596","source":"Crossref","is-referenced-by-count":6,"title":["Data mining technology in the analysis of college students\u2019 psychological problems"],"prefix":"10.2298","volume":"19","author":[{"given":"Jia","family":"Yu","sequence":"first","affiliation":[{"name":"NanChang JiaoTong Institute, Nanchang, Jiangxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"JingJing","family":"Lin","sequence":"additional","affiliation":[{"name":"NanChang JiaoTong Institute, Nanchang, Jiangxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1078","reference":[{"key":"ref1","unstructured":"Zhao Ding. Research on emotional data analysis and psychological early warning system based on data mining and intelligent computing [J]. Electronic production, 2021 (02): 88-90"},{"key":"ref2","unstructured":"Xu Meijuan, Xiao Xiong, xiong Ye, Dong Liangliang. Analysis of the \"heart\" way out of data mining technology in college mental health education [J]. Science and technology and innovation, 2020 (22): 43-44 + 47"},{"key":"ref3","unstructured":"Dai Qun, Wang Yibo. Research on academic early warning system of college students based on data mining [J]. Rural staff, 2020 (20): 230-231"},{"key":"ref4","unstructured":"Liang Yueying, Yuan Fang, Zhang Jian. Analysis of research hotspots of health education for college students at home and abroad based on data mining [J]. Chinese Journal of multimedia and network teaching (first ten issues), 2020 (10): 46-48"},{"key":"ref5","unstructured":"Yang Xun. Research on early warning of College Students' Psychological Crisis Based on big data technology [J]. Digital world, 2020 (10): 88-89"},{"key":"ref6","unstructured":"Chang Ni, Yu Yanjun. Dilemma and countermeasure analysis of psychological crisis early warning in Higher Vocational Colleges from the perspective of big data [J]. Intelligence, 2020 (26): 92-94"},{"key":"ref7","unstructured":"Gu Yongcheng. Analysis and Research on students' mental health based on Internet behavior data [D]. Guangdong University of technology, 2020"},{"key":"ref8","unstructured":"Fu ronghua. Application of association algorithm based on decision tree in rural college students' information system [J]. Hubei Agricultural Sciences, 2020,59 (10): 150-153 + 158"},{"key":"ref9","unstructured":"Chen haoquan, Hu Ruiyu, Zhao Zheng, Wang Shi. Design of mental health problem prevention platform based on data mining and database [J]. Science and technology horizon, 2020 (14): 49-51"},{"key":"ref10","unstructured":"Lin Jingyi, Li Dakun, Wu Pingxin, Wang Xu, Zhou Yan. Modeling and analysis of mental health early warning based on social data mining [J]. Electronic technology and software engineering, 2020 (08): 172-173"}],"container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:29:42Z","timestamp":1691742582000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02142200044Y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":10,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022]]}},"URL":"https:\/\/doi.org\/10.2298\/csis210404044y","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}